Quantitative methods are applicable for IA thinking be it for hypothesis generation, instrumentation, data collection and analysis of information at scales never before possible with insights that are comparable over time, generalizable and extensible. Quantitative skills can allow IAs to interpret and analyze others’ designs and research more readily, as well as combine methods and models for meta-analysis to help IAs move from description to prediction in designing and developing future interfaces and architectures. This presentation will review why you should use quantitative methods and discuss both foundational and emerging ideas that are applicable for content analysis, behavioral modeling, social media usage, informetrics and other IA-related issues. http://donturn.com or http://twitter.com/donturn

1. Quantitative Information
Architecture
Don Turnbull, Ph.D.
twitter.com/donturn
#quantia
donturn@gmail.com
http://donturn.com

2. whois?
• Software Developer
• M.S. @ Georgia Tech
• Software Design & Management
• Ph.D. @ U Toronto
• Principal @ Startup (Google)
• Faculty @ U Texas (Austin)
• Consultant & Entrepreneur

3. Ways of Knowing & Doing
The fox knows many things, but the
hedgehog knows one big thing. Archilochus
• Quantitative IA is one big thing: a
specialty, a mindset
• Designing appropriate experiments
• Leveraging existing quantitative data
• Conducting rigorous analysis

4. What does the world look
like to a Quantitative
Researcher?

6. Quantitative Methods
• What’s Quantitative good for?
• Understanding what users actually do
instead of what they said they do.
• Making comparisons over time
• Generalizable and extensible
• Useful for interpreting and analyzing
others results

7. Why you should go Quant
• It is a discipline
• Hypothesis-based
• More applicable to (peer) review
• It requires a set of skills that have a (much)
higher market value
• It examines characteristics that are constants
• Behavior
• Physical traits & abilities

8. The Power of Quant
• Fight fire with Fire
• Numbers speak the language of business
& technology (C-level execs)
• (Almost) infallible results
• Qualitative decisions for Quantitative
measurement

9. Predict everything?

10. What about Qualitative?
• Anyone can do qualitative research…
• …and anyone does
• Hard to replicate, hard to validate, easier
to do
• Domain of study (who) is main focus
• Variability is often wide
• Technique is critical

11. Quantitative & Qualitative should
complement each other

12. Because if they don’t

13. Things go wrong - fast

14. Why Quant, Why Now?
• Computational power & networked systems
• We need new modeling techniques, even
new metaphors to examine the complex
systems we interact with
• Finance, Psychology, Physics & Computer
Science
• Verifiable or provable by means of
observation or experiment: empirical laws.

15. The Network Effect(s)
• Understanding how people interact
• Metcalfe
• Milgram
• Wellman, Watts & others

16. A (new) Era of
Instrumentation
• We are undoubtedly in a new era of
reasoning
• Scientific Engineering enabled the
original Age of Reason
• Now to understand intent & interactions

17. Statistics

20. Statistics & every day life
• Weather
• Stock Market
• Whole channels on TV devoted to
both
• Sports…. All the time…. Everywhere
• Your Net Worth, IQ, Postal Code, SAT
score, GPA...

21. Data Science

23. A New Kind of (Empirical)
Science

24. What’s next ?

25. Analytics

26. HCIR (IRUX)

27. Atomic Information
Architecture (a marketplace)

28. Pervasive, Emotive &
Suggestive

29. Summary
New Age of (Empirical) Reason
Quant at scale = new Qual insights
Data Science (& its affect on IA/UX)
What’s next isn’t that far ahead…

30. Thanks
donturn@gmail.com
http://donturn.com
twitter.com/donturn